{"id":21072,"url":"https://github.com/cloneisme/awesome-ml-papers","name":"awesome-ml-papers","description":"Awesome Machine Learning Papers","projects_count":53,"last_synced_at":"2026-07-26T03:00:26.884Z","repository":{"id":126098823,"uuid":"477502488","full_name":"cloneisme/awesome-ml-papers","owner":"cloneisme","description":"Awesome Machine Learning Papers","archived":false,"fork":false,"pushed_at":"2022-04-06T22:39:16.000Z","size":65,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-07-07T08:02:53.617Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cloneisme.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2022-04-04T00:34:48.000Z","updated_at":"2023-05-07T12:20:03.000Z","dependencies_parsed_at":null,"dependency_job_id":"2e6c4b1b-4c7a-4930-ad82-7a4d507eb463","html_url":"https://github.com/cloneisme/awesome-ml-papers","commit_stats":null,"previous_names":["alan-minchan-kim/awesome-ml-papers","thetist/awesome-ml-papers","cloneisme/awesome-ml-papers"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cloneisme/awesome-ml-papers","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloneisme%2Fawesome-ml-papers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloneisme%2Fawesome-ml-papers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloneisme%2Fawesome-ml-papers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloneisme%2Fawesome-ml-papers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cloneisme","download_url":"https://codeload.github.com/cloneisme/awesome-ml-papers/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cloneisme%2Fawesome-ml-papers/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35898942,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-07-26T02:00:06.503Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T12:55:59.897Z","updated_at":"2026-07-26T03:00:26.884Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["SR","CNN","Transformer","Face Swapping","Normalization","GAN","Contrastive Learning","Flow","NeRF","3DMM","RNN","AE","Diffusion","Representation Learning","INR","Face Recognition","Facial Editting","Talking Head","Background Editting"],"sub_categories":[],"readme":"# Awesome Machine Learning Papers\n\n## Contributing\n\nmarkdown format:\n``` markdown\n**Here is the Paper Name.**\u003cbr\u003e\n*[Author 1](homepage), Author 2, and Author 3.*\u003cbr\u003e\nConference or Journal Year. [[PDF](link)] [[Project](link)] [[Github](link)] [[Video](link)] [[Data](link)]\n```\n\n## CNN\n- (ResNet) [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)\n\n## RNN\n- (GRU) [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://arxiv.org/abs/1412.3555)\n\n## Transformer\n- (Transformer) [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n- (ViT) [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)\n- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)\n\n## AE\n- (VAE) [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114)\n\n## GAN\n- (GAN) [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661)\n- (ProgressiveGAN) [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/abs/1710.10196)\n- (StyleGAN) [A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/abs/1812.04948)\n- (StyleGAN2) [Analyzing and Improving the Image Quality of StyleGAN](https://arxiv.org/abs/1912.04958)\n- (StyleGAN2-Ada) [Training Generative Adversarial Networks with Limited Data](https://arxiv.org/abs/2006.06676)\n- (StyleGAN3) [Alias-Free Generative Adversarial Networks](https://nvlabs.github.io/stylegan3/) \n- [StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2](https://arxiv.org/abs/2112.14683)\n\n## Flow\n- (Normalizing Flow) [Variational Inference with Normalizing Flows](https://ar5iv.labs.arxiv.org/html/1505.05770)\n- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://arxiv.org/abs/1807.03039)\n\n## Diffusion\n- (DDPM) [Diffusion Models Beat GANs on Image Synthesis](https://arxiv.org/pdf/2105.05233v4.pdf)\n\n## 3DMM\n\u003e 3D Morphable Model\n- (BFM) [Morphable Face Models - An Open Framework](https://arxiv.org/abs/1709.08398)\n- (FLAME) [Learning a model of facial shape and expression from 4D scans](https://ps.is.mpg.de/uploads_file/attachment/attachment/400/paper.pdf)\n- [SMPL: A Skinned Multi-Person Linear Model](https://files.is.tue.mpg.de/black/papers/SMPL2015.pdf)\n\n## NeRF\n\u003e Neural Radiance Field\n- [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://arxiv.org/abs/2003.08934)\n- (Instant-NDP) [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.pdf)\n- (DietNeRF) [Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis](https://ajayj.com/dietnerf)\n- [Point-NeRF: Point-based Neural Radiance Fields](https://xharlie.github.io/projects/project_sites/pointnerf/index.html)\n- [StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation](https://arxiv.org/abs/2112.11427)\n- [A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose](https://openreview.net/pdf?id=lwwEh0OM61b)\n\n## Normalization\n- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n- [Layer Normalization](https://arxiv.org/abs/1607.06450)\n\n## Representation Learning\n- [Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph](https://arxiv.org/abs/2202.12307)\n\n## Contrastive Learning\n- (SimCLR) [A Simple Framework for Contrastive Learning of Visual Representations](https://arxiv.org/abs/2002.05709)\n- (SimCLR2) [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://arxiv.org/abs/2006.10029)\n\n## INR\n\u003e Implicit Neural Representation\n- (Functa) [From data to functa: Your data point is a function and you should treat it like one](https://arxiv.org/abs/2201.12204)\n\n## SR\n\u003e Super Resolution\n- (GPEN) [GAN Prior Embedded Network for Blind Face Restoration in the Wild](https://arxiv.org/abs/2105.06070)\n\n## Face Recognition\n- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)\n- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://arxiv.org/abs/2004.00288)\n- [GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition](https://arxiv.org/abs/2005.10497)\n\n## Facial Editting\n- (STIT) [Stitch it in Time: GAN-Based Facial Editing of Real Videos](https://stitch-time.github.io/)\n\n## Talking Head\n- (SAFA) [SAFA: Structure Aware Face Animation](https://arxiv.org/abs/2111.04928)\n\n## Face Swapping\n- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://arxiv.org/abs/2106.06340)\n- [HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping](https://arxiv.org/abs/2106.09965)\n- [FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.pdf)\n- [One Shot Face Swapping on Megapixels](https://arxiv.org/abs/2105.04932)\n- [ShapeEditer: a StyleGAN Encoder for Face Swapping](https://arxiv.org/abs/2106.13984)\n- [FaceController: Controllable Attribute Editing for Face in the Wild](https://arxiv.org/abs/2102.11464)\n- [High-Resolution Neural Face Swapping for Visual Effects](https://studios.disneyresearch.com/wp-content/uploads/2020/06/High-Resolution-Neural-Face-Swapping-for-Visual-Effects.pdf)\n- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://arxiv.org/abs/1908.05932)\n- [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://arxiv.org/abs/1912.13457)\n- [Towards Open-Set Identity Preserving Face Synthesis](https://arxiv.org/abs/1803.11182)\n- [RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces](https://arxiv.org/abs/1804.03447)\n- [FSNet: An Identity-Aware Generative Model for Image-based Face Swapping](https://arxiv.org/abs/1811.12666)\n- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://arxiv.org/abs/2005.05535)\n- [Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation](https://arxiv.org/abs/2003.00813)\n- [On Face Segmentation, Face Swapping, and Face Perception](https://arxiv.org/abs/1704.06729)\n- SMILE\n- Smooth-Swap\n- FaceSwappingGAN\n- SberSwap\n\n## Background Editting\n- [Omnimatte: Associating Objects and Their Effects in Video](https://omnimatte.github.io/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/cloneisme%2Fawesome-ml-papers/projects"}